Abstract
Image denoising by minimizing a similarity of neighborhood-based cost function is presented. This cost function consists of two parts, one related to data fidelity and the other is a structure preserving smoothing term. The latter is controlled by a weight coefficient that measures the neighborhood similarity between two pixels and attaching an additional term penalizes it. Unlike most work in noise removal area, the weight of each pixel within the neighborhood is not defined by a Gaussian function. The obtained results show a good performance of our proposal, compared with some state-of-the-art algorithms.
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© 2011 Springer-Verlag Berlin Heidelberg
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Calderon, F., Júnez–Ferreira, C.A. (2011). Regularization with Adaptive Neighborhood Condition for Image Denoising. In: Batyrshin, I., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2011. Lecture Notes in Computer Science(), vol 7095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25330-0_35
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DOI: https://doi.org/10.1007/978-3-642-25330-0_35
Publisher Name: Springer, Berlin, Heidelberg
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